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AAAI
1997
13 years 9 months ago
Effective Bayesian Inference for Stochastic Programs
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
Daphne Koller, David A. McAllester, Avi Pfeffer
CVPR
2010
IEEE
14 years 4 months ago
Beyond Trees: MRF Inference via Outer-Planar Decomposition
Maximum a posteriori (MAP) inference in Markov Random Fields (MRFs) is an NP-hard problem, and thus research has focussed on either finding efficiently solvable subclasses (e.g. t...
Dhruv Batra, Andrew Gallagher, Devi Parikh, Tsuhan...
ICML
2007
IEEE
14 years 8 months ago
Efficient inference with cardinality-based clique potentials
Many collective labeling tasks require inference on graphical models where the clique potentials depend only on the number of nodes that get a particular label. We design efficien...
Rahul Gupta, Ajit A. Diwan, Sunita Sarawagi
JMLR
2010
148views more  JMLR 2010»
13 years 2 months ago
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation
We introduce novel results for approximate inference on planar graphical models using the loop calculus framework. The loop calculus (Chertkov and Chernyak, 2006b) allows to expre...
Vicenç Gómez, Hilbert J. Kappen, Mic...
IJCAI
2001
13 years 9 months ago
Approximate inference for first-order probabilistic languages
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Hanna Pasula, Stuart J. Russell